POS1062 HARNESSING THE POWER OF MACHINE LEARNING TO PREDICT REMISSION IN PATIENTS WITH PSORIATIC ARTHRITIS ON SECUKINUMAB: IMPLEMENTATION AND VALIDATION OF A CANDIDATE ALGORITHM ON 121 PATIENTS
Authors: V. Venerito, M. Fornaro, F. Cacciapaglia, S. Tangaro, G. Lopalco, F. Iannone
Journal: Annals of the Rheumatic Diseases (2022)

Background Although novel therapies with biotechnological agents and small molecules may lead to the complete clearing of psoriasis in the vast majority of patients, the latter drugs only allow Psoriatic Arthritis (PsA) disease control in up to 50% of patients (1). In an increasing number of clinical scenarios, machine learning (ML) is emerging as a tool for the implementation of multi-parametric decision algorithms. ML allows to handle complex non-linear relationships between patient attributes that are hard to model with traditional statistical methods, merging them to output a forecast or a probability for a given outcome, enabling personalized medicine (2). Objectives We aimed to develop a ML algorithm capable of predicting the probability of remission in PsA patients on Secukinumab to support clinicians in choosing the optimal treatment strategy. Methods Patients with classified PsA according to CASPAR criteria undergoing Secukinumab treatment between September 2017 and September 2020 at our tertiary Centre were retrospectively observed.Either at treatment baseline and at 12-month follow up, we retrieved demographic and clinical characteristics, including Body Mass Index (BMI), disease phenotypes, Disease Activity in PsA (DAPSA), Leeds Enthesitis Index (LEI) and Ankylosing Spondylitis Disease Activity Score (ASDAS, on C-Reactive Protein). After a ML variable selection method, based on an eXtreme Gradient Boosting (XGBoost) wrapper, an attribute core set with the least number of predictors was used for implementing n.3 ML algorithms, namely Logistic Regression (LR), Decision Trees (DT) and XGBoost. Each algorithm was trained and validated with 10-fold cross-validation. The performance of each algorithm in both phases was assessed in terms of of accuracy and area under receiver operating characteristic curve (AUROC). Results The dataset consisted of n.121 PsA patients (62/121 female, 51.2%), with mean age (±SD) 52.9±10.1 years and mean disease duration of 5.9 ±10.4 years. Twenty-five of them (20.7%) had axial involvement whereas 88/121 (72.7%) had polyarticular involvement. Psoriasis was present in 84/121 patients (69.4%). At baseline, mean DAPSA was 14.9 ± 9.2, mean HAQ-DI 1.1 ± 0.7, mean LEI 0.6 ± 1, mean ASDAS 2.5 ± 0.8, mean PASI 2 ± 2.9, mean BMI 28.4 ± 4.9 . Secukinumab at 300 mg dose was administered to 79/121 patients (65.3%). At 12 months DAPSA remission was achieved by 24/121 patients (19.8%). Accuracy of LR, DT and XGBoost was of 0.70 ± 0.11, 0.81 ± 0.07 and 0.89 ± 0.05, respectively. Consistently AUROC (Figure 1 Panels ABC) were 0.63 ± 0.2, 0.79 ± 0.2 and 0.93 ± 0.1, respectively. A sample decision tree explaining XGBoost algorithm function has been provided (Figure 1 Panel D). LEI and DAPSA at baseline were shown as the most important attributes for such algorithm (Figure 1 Panel E). Figure 1. Conclusion ML can support Rheumatologists in profiling those patients more likely to respond to Secukinumab. References [1]Scher JU, Ogdie A, Merola JF, Ritchlin C. Preventing psoriatic arthritis: focusing on patients with psoriasis at increased risk of transition. Nat Rev Rheumatol. 2019 Mar;15(3):153-166. doi: 10.1038/s41584-019-0175-0. [2]Venerito V, Angelini O, Cazzato G, Lopalco G, Maiorano E, Cimmino A, Iannone F. A convolutional neural network with transfer learning for automatic discrimination between low and high-grade synovitis: a pilot study. Intern Emerg Med. 2021 Sep;16(6):1457-1465. doi: 10.1007/s11739-020-02583-x. Epub 2021 Jan 2. Disclosure of Interests Vincenzo Venerito Speakers bureau: Abbvie, Paid instructor for: Pfizer, Lilly, Marco Fornaro: None declared, Fabio Cacciapaglia Speakers bureau: Lilly, Abbvie, BMS. Pfizer, Paid instructor for: Lilly, Sabina Tangaro: None declared, Giuseppe Lopalco Speakers bureau: SOBI NOVARTIS BMS ABBVIE, Paid instructor for: PFIZER, Florenzo Iannone Speakers bureau: Abbvie Pfizer UCB BMS Galapagos Novartis Lilly SOBI ROCHE, Paid instructor for: pfizer